{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HLaIUSAGUjt"
      },
      "source": [
        "# 下载比赛数据"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "HThTCeEq-n1m",
        "outputId": "4343b1eb-63d3-4d8c-d3e9-0e41d39c0b73"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2024-07-15 03:12:12--  https://drive.usercontent.google.com/download?id=1JwR0Q1ArTg6c47EF2ZuIBpQwCPgXKrO2&export=download&authuser=0&confirm=t&uuid=dc3aa13c-c3a9-458f-983a-8586798cb635&at=APZUnTX25XMxi-z-3wBcgR93IGsL%3A1719235792953\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 74.125.135.132, 2607:f8b0:400e:c01::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|74.125.135.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1084182095 (1.0G) [application/octet-stream]\n",
            "Saving to: ‘Dataset.zip’\n",
            "\n",
            "Dataset.zip         100%[===================>]   1.01G   128MB/s    in 12s     \n",
            "\n",
            "2024-07-15 03:12:26 (85.7 MB/s) - ‘Dataset.zip’ saved [1084182095/1084182095]\n",
            "\n",
            "Archive:  Dataset.zip\n",
            "   creating: Dataset/Dataset/\n",
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            "  inflating: Dataset/Dataset/Training_data/Label/press_649.npy  \n",
            "  inflating: Dataset/Dataset/Training_data/Label/press_651.npy  \n",
            "  inflating: Dataset/Dataset/Training_data/Label/press_652.npy  \n",
            "  inflating: Dataset/Dataset/Training_data/Label/press_654.npy  \n",
            "  inflating: Dataset/Dataset/Training_data/Label/press_655.npy  \n",
            "  inflating: Dataset/Dataset/Training_data/Label/press_656.npy  \n",
            "  inflating: Dataset/Dataset/Training_data/Label/press_657.npy  \n",
            "  inflating: Dataset/Dataset/Training_data/train_pressure_min_std.txt  \n",
            "  inflating: Dataset/Dataset/Training_data/watertight_global_bounds.txt  \n",
            "  inflating: Dataset/Dataset/Training_data/watertight_meshes.txt  \n"
          ]
        }
      ],
      "source": [
        "!wget --header=\"Host: drive.usercontent.google.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Cookie: __Secure-ENID=12.SE=Yd0Bj-CLJ14fnd4qzdJHmwUs4B5zz46UaPC1cPJigNqqFV9PtM2CYyBpSbCkOyzUwzlEdZ1nZFf-igtGi7wSdJ_gqQSfQfh84r9egqFQAy9-GKayCRbdQKdera-2mkpuIT-c64CyR9vfNojM3hxZ9Dej-dGvtxlGjal9ttEHybw; __gsas=ID=ae0421b9a34b478c:T=1710758437:RT=1710758437:S=ALNI_MZP13R9ZOHbCzC0rgHSMrGXj6GCsg; HSID=A-4I-ZudDNUIB6EKH; SSID=A7v_1v9un6xAwVNku; APISID=ctK8IbLjeuDUmgys/AFnMSLWt9KddceDI6; SAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; __Secure-1PAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; __Secure-3PAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; SID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_kzuBV1TvOhAIC8VF1e9fpgACgYKATQSARQSFQHGX2Mi8LXUwWoIwNCEPU8Sy3mXUxoVAUF8yKqGXVfjTGz9gQal7nwGr4Pl0076; __Secure-1PSID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_PDa-DzVmbdGFPyxMQpk9_QACgYKAewSARQSFQHGX2MiAeee4fn0OWglWZfAygqkyBoVAUF8yKp-Sfmtnueimxc-0QbJRF9I0076; __Secure-3PSID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_g9IrMeU98APBo9Stp6wEnAACgYKAQASARQSFQHGX2MiFWtc9ucONXnpxBzlRdudEhoVAUF8yKoeZwCpJDnjfAFjGssHSUGm0076; NID=515=GQhY9nKKFCx3qFDjE0MA4ubjWNdef6xCIY_RfWOPWKEtyfBN3nAUl8WHI2VczjNQ4rVkj1XBAY8WNWHXyqSK10CfT4FxsFlPzrHIJpeTtm1nWRNBd9AAfBKJHz4XpESszntVUTE_59RklZuKo0vk1poReVi2da1PZKC3CTKH2Ll3gB5xuB9wf4bmq8ylVUuIROPJczr0XnCuUHV3qLdBvgy9_870b6UwOq1iOlIxFQFm01EZ4pqF4q1Ub3QRSWpEMLh4LSZFpJ5O255R5OV7krmEdDvH_sHoTEPZAg2PoEpwAyGK6Xp9qcLIlldgx5-5V86N8Wtb93uTlQuA_CFXb5_2eP3bgeX8txwlJ5SrldVjg9ctzYtBU2RwJKTSvdHfIG7lpOkg6XlkvDOcJpR3DihT_OlqnPn7drCAJpvVDv29hZn5XPMXaSrNdbG64OJ9urJEw5odEwsLYkkpC1vmlUcuoo52S5f6RQu0Z8kZiV8iRW6XIqHsSmQHunVaxk6xWCStUg; __Secure-1PSIDTS=sidts-CjEB3EgAEtTS0OazynCofIH4RCBstiRP5flEcvYW3z4Fg9oGd5QOESDOZt1wO2iqUYHjEAA; __Secure-3PSIDTS=sidts-CjEB3EgAEtTS0OazynCofIH4RCBstiRP5flEcvYW3z4Fg9oGd5QOESDOZt1wO2iqUYHjEAA; SIDCC=AKEyXzVI6aMX8lSDja86Yts3FBAtBzPCzVNgaX5BCz78NWsWzlT3yFWKUV7ZE46SFzE1GiBI-cHdTw; __Secure-1PSIDCC=AKEyXzUo4NQAwqqPMxP2eye-MFEbZmBIm_sZqRU1amttg0YoQkc8ZKSNXdHl5jNCMEbhrUHhS9-K; __Secure-3PSIDCC=AKEyXzWf2lIdmDLeZKpXSi9GytVQb6XudrYiNUBA5gW952YuLh8kL6T3IbBlu8zOTfGEcdUp5O1R\" --header=\"Connection: keep-alive\" \"https://drive.usercontent.google.com/download?id=1JwR0Q1ArTg6c47EF2ZuIBpQwCPgXKrO2&export=download&authuser=0&confirm=t&uuid=dc3aa13c-c3a9-458f-983a-8586798cb635&at=APZUnTX25XMxi-z-3wBcgR93IGsL%3A1719235792953\" -c -O 'Dataset.zip'\n",
        "!mkdir -p Dataset\n",
        "!unzip -o Dataset.zip -d Dataset/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lMoeDzSQGXXr"
      },
      "source": [
        "# 安装相关依赖"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "zgcuMKZhA7Au",
        "outputId": "5acba52e-2c37-4058-87b3-06d010c3a750"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 1)) (4.66.4)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 2)) (2.0.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 3)) (1.25.2)\n",
            "Collecting torch_geometric (from -r requirements.txt (line 4))\n",
            "  Downloading torch_geometric-2.5.3-py3-none-any.whl (1.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 5)) (1.2.2)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 6)) (4.41.2)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 7)) (2.4.0)\n",
            "Collecting openmesh (from -r requirements.txt (line 8))\n",
            "  Downloading openmesh-1.2.1.tar.gz (9.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.3/9.3 MB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting flash-attn==2.5.9.post1 (from -r requirements.txt (line 9))\n",
            "  Downloading flash_attn-2.5.9.post1.tar.gz (2.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m48.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from flash-attn==2.5.9.post1->-r requirements.txt (line 9)) (2.3.0+cu121)\n",
            "Collecting einops (from flash-attn==2.5.9.post1->-r requirements.txt (line 9))\n",
            "  Downloading einops-0.8.0-py3-none-any.whl (43 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.2/43.2 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->-r requirements.txt (line 2)) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->-r requirements.txt (line 2)) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->-r requirements.txt (line 2)) (2024.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from torch_geometric->-r requirements.txt (line 4)) (1.11.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch_geometric->-r requirements.txt (line 4)) (2023.6.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch_geometric->-r requirements.txt (line 4)) (3.1.4)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from torch_geometric->-r requirements.txt (line 4)) (3.9.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torch_geometric->-r requirements.txt (line 4)) (2.31.0)\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.10/dist-packages (from torch_geometric->-r requirements.txt (line 4)) (3.1.2)\n",
            "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from torch_geometric->-r requirements.txt (line 4)) (5.9.5)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->-r requirements.txt (line 5)) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->-r requirements.txt (line 5)) (3.5.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers->-r requirements.txt (line 6)) (3.15.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from transformers->-r requirements.txt (line 6)) (0.23.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers->-r requirements.txt (line 6)) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers->-r requirements.txt (line 6)) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers->-r requirements.txt (line 6)) (2024.5.15)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers->-r requirements.txt (line 6)) (0.19.1)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers->-r requirements.txt (line 6)) (0.4.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers->-r requirements.txt (line 6)) (4.12.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->-r requirements.txt (line 2)) (1.16.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric->-r requirements.txt (line 4)) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric->-r requirements.txt (line 4)) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric->-r requirements.txt (line 4)) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric->-r requirements.txt (line 4)) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric->-r requirements.txt (line 4)) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric->-r requirements.txt (line 4)) (4.0.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch_geometric->-r requirements.txt (line 4)) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric->-r requirements.txt (line 4)) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric->-r requirements.txt (line 4)) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric->-r requirements.txt (line 4)) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric->-r requirements.txt (line 4)) (2024.7.4)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9)) (1.13.0)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9)) (3.3)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9))\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9))\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9))\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9))\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9))\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9))\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9))\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9))\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9))\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.20.5 (from torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9))\n",
            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9))\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9)) (2.3.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9))\n",
            "  Downloading nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m63.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->flash-attn==2.5.9.post1->-r requirements.txt (line 9)) (1.3.0)\n",
            "Building wheels for collected packages: flash-attn, openmesh\n",
            "  Building wheel for flash-attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for flash-attn: filename=flash_attn-2.5.9.post1-cp310-cp310-linux_x86_64.whl size=120889689 sha256=5022ba11d48bf74926da9c16260f4ea2b9bb7f4e29bdb4bd6e1383ad1c55d16f\n",
            "  Stored in directory: /root/.cache/pip/wheels/cc/ad/f6/7ccf0238790d6346e9fe622923a76ec218e890d356b9a2754a\n",
            "  Building wheel for openmesh (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for openmesh: filename=openmesh-1.2.1-cp310-cp310-linux_x86_64.whl size=855149 sha256=ecb383be504d5ecbb8cd78ee539b5c8a408a63f2761d86d67eb346f14e4ec3ff\n",
            "  Stored in directory: /root/.cache/pip/wheels/05/52/ac/4cf307e2dac381ab093a0390901305aee1110ca733ebf55d55\n",
            "Successfully built flash-attn openmesh\n",
            "Installing collected packages: openmesh, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, einops, nvidia-cusparse-cu12, nvidia-cudnn-cu12, torch_geometric, nvidia-cusolver-cu12, flash-attn\n",
            "Successfully installed einops-0.8.0 flash-attn-2.5.9.post1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 openmesh-1.2.1 torch_geometric-2.5.3\n"
          ]
        }
      ],
      "source": [
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDq5HenMLUzP",
        "outputId": "44b05e36-c078-4469-c709-77be503918e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in links: https://data.pyg.org/whl/torch-2.3.0+cu121.html\n",
            "Collecting torch_scatter\n",
            "  Downloading https://data.pyg.org/whl/torch-2.3.0%2Bcu121/torch_scatter-2.1.2%2Bpt23cu121-cp310-cp310-linux_x86_64.whl (10.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.9/10.9 MB\u001b[0m \u001b[31m73.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torch_sparse\n",
            "  Downloading https://data.pyg.org/whl/torch-2.3.0%2Bcu121/torch_sparse-0.6.18%2Bpt23cu121-cp310-cp310-linux_x86_64.whl (5.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.1/5.1 MB\u001b[0m \u001b[31m94.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torch_cluster\n",
            "  Downloading https://data.pyg.org/whl/torch-2.3.0%2Bcu121/torch_cluster-1.6.3%2Bpt23cu121-cp310-cp310-linux_x86_64.whl (3.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m61.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from torch_sparse) (1.11.4)\n",
            "Requirement already satisfied: numpy<1.28.0,>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from scipy->torch_sparse) (1.25.2)\n",
            "Installing collected packages: torch_scatter, torch_sparse, torch_cluster\n",
            "Successfully installed torch_cluster-1.6.3+pt23cu121 torch_scatter-2.1.2+pt23cu121 torch_sparse-0.6.18+pt23cu121\n"
          ]
        }
      ],
      "source": [
        "!pip install torch_scatter torch_sparse torch_cluster -f https://data.pyg.org/whl/torch-2.3.0+cu121.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YpOPR2IoGb1n"
      },
      "source": [
        "# 导入所需包"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aAgaaFpTEABI"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random\n",
        "from tqdm import tqdm\n",
        "\n",
        "import torch_geometric.transforms as T\n",
        "from torch_geometric import seed_everything\n",
        "from torch_geometric.loader import DataLoader\n",
        "from torch_geometric.io import read_ply\n",
        "from torch_geometric.nn import MessagePassing, radius_graph, Linear\n",
        "from torch_geometric.nn import global_max_pool\n",
        "from torch_geometric.nn import PointNetConv, PointTransformerConv\n",
        "\n",
        "import torch\n",
        "from torch.cuda import amp\n",
        "from torch import Tensor\n",
        "from torch.nn import Sequential, ReLU\n",
        "from torch import nn\n",
        "\n",
        "from transformers import BertModel, BertConfig\n",
        "\n",
        "root = 'Dataset'\n",
        "seed_everything(2024)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wol9a7dhET6R"
      },
      "source": [
        "# 加载数据"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M8Ngpm49EV-U"
      },
      "outputs": [],
      "source": [
        "train_ids = np.loadtxt(f\"{root}/Dataset/Training_data/watertight_meshes.txt\", dtype=str)\n",
        "valid_index = np.array([ 15,  21,  44,  71,  80,  85,  98, 111, 120, 126, 133, 143, 147,\n",
        "                        149, 154, 161, 164, 168, 169, 198, 222, 223, 231, 238, 251, 258,\n",
        "                        265, 273, 280, 285, 286, 299, 320, 327, 344, 345, 354, 380, 390,\n",
        "                        407, 408, 420, 427, 440, 456, 460, 479, 484, 495, 496])\n",
        "train_index = np.setdiff1d(np.arange(len(train_ids)), valid_index)\n",
        "valid_ids = train_ids[valid_index]\n",
        "train_ids = train_ids[train_index]\n",
        "test_ids = os.listdir(f\"{root}/Dataset/Testset_track_A/Inference\")\n",
        "test_ids = sorted([i[i.find('_')+1:i.find('.')] for i in test_ids if 'mesh_' in i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "02oyl5q5Eex_"
      },
      "outputs": [],
      "source": [
        "with open(f'{root}/Dataset/Training_data/train_pressure_min_std.txt') as fp:\n",
        "    mean = fp.readline().split(\" \")\n",
        "    std = fp.readline().split(\" \")\n",
        "    mean = float(mean[0])\n",
        "    std = float(std[0])\n",
        "\n",
        "def read_ply_as_data(ids, training=True):\n",
        "    data_list = []\n",
        "    for file_id in tqdm(ids):\n",
        "        press = None\n",
        "        if training:\n",
        "            ply_path = f\"{root}/Dataset/Training_data/Feature/mesh_{file_id}.ply\"\n",
        "            press_path = f\"{root}/Dataset/Training_data/Label/press_{file_id}.npy\"\n",
        "            press = np.load(press_path).reshape(-1)\n",
        "            press = np.concatenate((press[0:16], press[112:]), axis=0).reshape(-1)\n",
        "            press = (press - mean) / std\n",
        "            press = torch.tensor(press).to(torch.float)\n",
        "        else:\n",
        "            ply_path = f\"{root}/Dataset/Testset_track_A/Inference/mesh_{file_id}.ply\"\n",
        "\n",
        "        data = read_ply(ply_path)\n",
        "        data.y = press\n",
        "        data = T.FaceToEdge()(data)\n",
        "        data = T.Distance()(data)\n",
        "        data = T.Center()(data)\n",
        "        data_list.append(data)\n",
        "    return data_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4NxsMPNEh5i",
        "outputId": "5ff683be-545f-4aa7-c243-e51589a5b516"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [00:03<00:00, 128.57it/s]\n",
            "100%|██████████| 50/50 [00:00<00:00, 83.23it/s]\n",
            "100%|██████████| 50/50 [00:00<00:00, 88.71it/s]\n"
          ]
        }
      ],
      "source": [
        "train_data = read_ply_as_data(train_ids)\n",
        "valid_data = read_ply_as_data(valid_ids)\n",
        "test_data = read_ply_as_data(test_ids, training=False)\n",
        "num_points = train_data[0].pos.size(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zc2s-D0eGMLk"
      },
      "source": [
        "# 模型定义"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cIAzVyF2GRxG"
      },
      "outputs": [],
      "source": [
        "class LpLoss(nn.Module):\n",
        "    def __init__(self, dim=None):\n",
        "        super().__init__()\n",
        "        self.dim = dim\n",
        "\n",
        "    def forward(self, x, y):\n",
        "        a = torch.norm(x - y, p=2, dim=self.dim)\n",
        "        b = torch.norm(y, p=2, dim=self.dim)\n",
        "        return (a / b).mean()\n",
        "\n",
        "class Bert(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.config = BertConfig(hidden_dropout_prob=0.,\n",
        "                                attention_probs_dropout_prob=0.,\n",
        "                                hidden_size=512,\n",
        "                                num_attention_heads=4,\n",
        "                                num_hidden_layers=12,\n",
        "                                max_position_embeddings=1,\n",
        "                                intermediate_size=2024,\n",
        "                                vocab_size=1)\n",
        "        self.bert = BertModel(self.config)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(-1, num_points,  self.config.hidden_size)\n",
        "        x = self.bert(inputs_embeds=x)[0]\n",
        "        x = x.view(-1, self.config.hidden_size)\n",
        "        return x\n",
        "\n",
        "class PointNetLayer(MessagePassing):\n",
        "    def __init__(self, in_channels: int, out_channels: int):\n",
        "        super().__init__(aggr='max')\n",
        "        self.mlp = Linear(in_channels, out_channels)\n",
        "\n",
        "    def forward(self,\n",
        "        x: Tensor,\n",
        "        pos: Tensor,\n",
        "        edge_index: Tensor,\n",
        "        edge_attr: Tensor,\n",
        "    ) -> Tensor:\n",
        "        # Start propagating messages.\n",
        "        h = self.propagate(edge_index, x=x, pos=pos, edge_attr=edge_attr)\n",
        "        x = self.mlp(h) # here\n",
        "        return x\n",
        "\n",
        "    def message(self,\n",
        "        x_i: Tensor,\n",
        "        x_j: Tensor,\n",
        "        pos_j: Tensor,\n",
        "        pos_i: Tensor,\n",
        "        edge_attr: Tensor,\n",
        "    ) -> Tensor:\n",
        "        edge_feat = torch.cat([x_j, pos_j - pos_i, x_i, pos_j, edge_attr], dim=-1)\n",
        "        return edge_feat\n",
        "\n",
        "class GNNBert(torch.nn.Module):\n",
        "    def __init__(self, num_layers=16):\n",
        "        super().__init__()\n",
        "\n",
        "        hidden = 128\n",
        "        convs = torch.nn.ModuleList()\n",
        "        self.mlp_in = Linear(3, 512)\n",
        "\n",
        "        for i in range(num_layers):\n",
        "            conv = PointNetLayer(-1, hidden)\n",
        "            convs.append(conv)\n",
        "        self.convs = convs\n",
        "        self.mlp_out = Linear(hidden, 512)\n",
        "        self.classifier = Linear(-1, 1)\n",
        "        self.bert = Bert()\n",
        "\n",
        "    def forward(self, data):\n",
        "        pos = data.pos\n",
        "        edge_index = data.edge_index\n",
        "        edge_attr = data.edge_attr\n",
        "        x = self.mlp_in(pos)\n",
        "        h = pos\n",
        "        for conv in self.convs:\n",
        "            h = conv(x=h, pos=pos, edge_index=edge_index, edge_attr=edge_attr).relu()\n",
        "        out1 = 0.4*self.classifier(self.mlp_out(h))\n",
        "        out2 = 0.6*self.classifier(self.bert(x))\n",
        "        return (out1 + out2).squeeze()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bZwDG_qPGY8y"
      },
      "source": [
        "# 模型训练"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KVO7j6kxRz6k"
      },
      "outputs": [],
      "source": [
        "do_train = True if not os.path.exists('model.pt') else False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "OZVDhsuaGang",
        "outputId": "67b01324-300b-45b1-8bfb-c9a6e84f843e"
      },
      "outputs": [
        {
          "ename": "RuntimeError",
          "evalue": "Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-438b212f6d82>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cuda'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGNNBert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdo_train\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mtrain_loader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mto\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1171\u001b[0m                     \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1173\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m     def register_full_backward_pre_hook(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn, recurse)\u001b[0m\n\u001b[1;32m    777\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    778\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m                 \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    780\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn, recurse)\u001b[0m\n\u001b[1;32m    802\u001b[0m             \u001b[0;31m# `with torch.no_grad():`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    803\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 804\u001b[0;31m                 \u001b[0mparam_applied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    805\u001b[0m             \u001b[0mp_should_use_set_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    806\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m   1157\u001b[0m                         \u001b[0mmemory_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_to_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1158\u001b[0m                     )\n\u001b[0;32m-> 1159\u001b[0;31m                 return t.to(\n\u001b[0m\u001b[1;32m   1160\u001b[0m                     \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m                     \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_complex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m    291\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m\"CUDA_MODULE_LOADING\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m             \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"CUDA_MODULE_LOADING\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"LAZY\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    294\u001b[0m         \u001b[0;31m# Some of the queued calls may reentrantly call _lazy_init();\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m         \u001b[0;31m# we need to just return without initializing in that case.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx"
          ]
        }
      ],
      "source": [
        "device = torch.device('cuda')\n",
        "model = GNNBert().to(device)\n",
        "\n",
        "if do_train:\n",
        "    train_loader = DataLoader(train_data, batch_size=1, shuffle=True)\n",
        "    valid_loader = DataLoader(valid_data, batch_size=64, shuffle=False)\n",
        "\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
        "\n",
        "    evaluator = LpLoss(dim=1)\n",
        "    loss_fn = torch.nn.MSELoss()\n",
        "    scaler = amp.GradScaler()\n",
        "    best = 1e6\n",
        "    epochs = 200\n",
        "\n",
        "    for epoch in tqdm(range(epochs)):\n",
        "        model.train()\n",
        "        epoch_loss = 0\n",
        "        for data in train_loader:\n",
        "            optimizer.zero_grad()\n",
        "            data = data.to(device)\n",
        "            with amp.autocast():\n",
        "                out = model(data)\n",
        "                loss = loss_fn(out, data.y)\n",
        "            scaler.scale(loss).backward()\n",
        "            scaler.step(optimizer)\n",
        "            scaler.update()\n",
        "            epoch_loss += loss\n",
        "        lr = optimizer.state_dict()['param_groups'][0]['lr']\n",
        "\n",
        "        model.eval()\n",
        "        score = []\n",
        "        with torch.no_grad():\n",
        "            val_loss = 0\n",
        "            for data in valid_loader:\n",
        "                data = data.to(device)\n",
        "                with amp.autocast():\n",
        "                    out = model(data)\n",
        "                    val_loss += loss_fn(out, data.y)\n",
        "                pred = out * std + mean\n",
        "                label = data.y * std + mean\n",
        "                pred = pred.view(-1, num_points)\n",
        "                label = label.view(-1, num_points)\n",
        "                score.append(evaluator(pred, label).item())\n",
        "        score = np.mean(score)\n",
        "\n",
        "        if score < best:\n",
        "            best = score\n",
        "            torch.save(model.state_dict(), f'model.pt')\n",
        "\n",
        "        print(f\"Valid score: {score:.5f} - lr {lr} - Best: {best:.5f}\")\n",
        "else:\n",
        "    print('Loading model from checkpoints...')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWQnRLxzGh-l"
      },
      "source": [
        "# 模型推理"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_qTAL-ERGjnQ"
      },
      "outputs": [],
      "source": [
        "track = 'gen_answer_A'\n",
        "submit_path = f'content/{track}'\n",
        "os.makedirs(submit_path, exist_ok=True)\n",
        "\n",
        "test_loader = DataLoader(test_data, batch_size=1, shuffle=False)\n",
        "model.load_state_dict(torch.load(f'model.pt'))\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    for idx, data in enumerate(test_loader):\n",
        "        data = data.to(device)\n",
        "        with amp.autocast():\n",
        "            out = model(data)\n",
        "        out = out.float().view(-1).cpu().numpy() * std + mean\n",
        "        file_id = test_ids[idx]\n",
        "        np.save(f'{submit_path}/press_{file_id}.npy', out)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wbl1uZioGrMe"
      },
      "outputs": [],
      "source": [
        "!zip -r B_result.zip content/gen_answer_A"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r1z7v-ZjIBK1"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
